We train all models in an unsupervised manner.
For node classification, we use a standard linear evaluation protocol [1]} to evaluate the quality of the embeddings learned by the model.
Specifically, the parameters and gradients of the model are frozen and the embeddings are used as input for classification using a logistic regression with the \(\ell _2\)  regularization.
In addition to using the public 20 divisions on the WikiCS dataset, we randomly divide the training set, validation set, and test set 20 times according to 10%, 10%, and 80% on the other datasets [2]}, and report the best round on average performance of the validation set as the evaluation result.
For graph classification, [3]} specifically states that downstream classifiers can significantly affect the evaluation results of unsupervised learning.
We therefore uniformly use the standard linear evaluation protocol instead of SVM to evaluate all models and use ten-fold cross-validation to evaluate the performance of graph classification.
